code: def test_no_positives(self):
      miner = UniformHistogramMiner()
      batch_size = 32
      for dtype in TEST_DTYPES:
          embeddings = (
              torch.randn(batch_size, 64).type(dtype).to(TEST_DEVICE)
          )
          labels = torch.arange(batch_size)
          a1, p, _, _ = miner(embeddings, labels)
          self.assertTrue(len(a1) == len(p) == 0)
  
file_path: pytorch-metric-learning/tests/miners/test_uniform_histogram_miner.py
imports: import unittest
  import torch
  from pytorch_metric_learning.distances import LpDistance, SNRDistance
  from pytorch_metric_learning.miners import UniformHistogramMiner
  from pytorch_metric_learning.utils import loss_and_miner_utils
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 2 for calling the TestUniformHistogramMiner obj's underlying member method code with example usage: obj.test_no_positives() without return types